Enhanced Tunicate Swarm Algorithm for Big Data Optimization
Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have beco...
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Sakarya University
2023-04-01
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| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/2735526 |
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| author | Emine Baş |
| author_facet | Emine Baş |
| author_sort | Emine Baş |
| collection | DOAJ |
| description | Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm (TSA), which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA (ETSA) was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes (10, 20, 30, 50, 100) and three different stopping criteria (300, 500, 1000). The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems. |
| format | Article |
| id | doaj-art-53ea6ee2f8b74b3dbdde03d4282312bc |
| institution | Kabale University |
| issn | 2147-835X |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| spelling | doaj-art-53ea6ee2f8b74b3dbdde03d4282312bc2024-12-23T08:15:22ZengSakarya UniversitySakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi2147-835X2023-04-0127231333410.16984/saufenbilder.119570028Enhanced Tunicate Swarm Algorithm for Big Data OptimizationEmine Baş0https://orcid.org/0000-0003-4322-6010KONYA TEKNİK ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİToday, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm (TSA), which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA (ETSA) was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes (10, 20, 30, 50, 100) and three different stopping criteria (300, 500, 1000). The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems.https://dergipark.org.tr/tr/download/article-file/2735526tsatunicatemeta-heuristicbig data |
| spellingShingle | Emine Baş Enhanced Tunicate Swarm Algorithm for Big Data Optimization Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi tsa tunicate meta-heuristic big data |
| title | Enhanced Tunicate Swarm Algorithm for Big Data Optimization |
| title_full | Enhanced Tunicate Swarm Algorithm for Big Data Optimization |
| title_fullStr | Enhanced Tunicate Swarm Algorithm for Big Data Optimization |
| title_full_unstemmed | Enhanced Tunicate Swarm Algorithm for Big Data Optimization |
| title_short | Enhanced Tunicate Swarm Algorithm for Big Data Optimization |
| title_sort | enhanced tunicate swarm algorithm for big data optimization |
| topic | tsa tunicate meta-heuristic big data |
| url | https://dergipark.org.tr/tr/download/article-file/2735526 |
| work_keys_str_mv | AT eminebas enhancedtunicateswarmalgorithmforbigdataoptimization |